In [18]:
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
import pandas as pd
# Calculate metrics
accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)
conf_matrix = confusion_matrix(y_true, y_pred)
# Labels for the matrix
labels = ['True Negative', 'False Positive', 'False Negative', 'True Positive']
# Convert to a numpy array for seaborn heatmap
cm_array = np.array(conf_matrix).reshape(2, 2)
# Create a DataFrame to display the metrics
metrics_df = pd.DataFrame({
'Metric': ['Accuracy', 'Precision', 'Recall', 'F1 Score'],
'Value': [f'{accuracy*100:.2f} %', f'{precision*100:.2f} %', f'{recall*100:.2f} %', f'{f1*100:.2f} %']
})
# Display the metrics DataFrame
print("\nClassifier Metrics:")
print(metrics_df)
# Create a heatmap
plt.figure(figsize=(4, 3))
sns.heatmap(cm_array, annot=True, fmt='d', cmap='Blues', xticklabels=['Predicted 0', 'Predicted 1'], yticklabels=['Actual 0', 'Actual 1'])
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.title('Confusion Matrix')
plt.show()
Classifier Metrics:
Metric Value
0 Accuracy 58.90 %
1 Precision 62.92 %
2 Recall 67.47 %
3 F1 Score 65.12 %
